How AI Can Make Christmas Shopping A Dream

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Using AI in retail forecasting gives us a completely granular view of different sales channels

Unsplash - Dieter de Vroomen

Shopping in the holidays may seem like chaos to you and me, but retailers have an arsenal of data-driven tools to define their strategy well in advance. The latest fashion trends, changes in the economy and even the weather all factor into what you see online and in-store, and the rise of e-commerce and monitoring technology has given retailers more insight into how we shop than ever before.

Consumer trends are not 100% reliable (where did fidget spinners come from?), but AI forecasting is helping retailers pull together the many influencing factors and make shopping a more personalized and enjoyable experience. As technology makes its presence known, the retail environment is set to become more focused on the consumer, and move closer towards Jeff Bezos’ vision of a ‘customer obsessed’ retail landscape.

Measuring how we shop

Our willingness to shop is affected by a huge number of factors that influence our feelings of security, spontaneity and overall happiness. While we haven’t yet managed to fully quantify human emotions - although AI is getting closer every day - it is possible to measure the factors that influence how we feel about spending or saving our money. Dr. Michael Haydock, IBM’s VP & Chief Scientist of Global Business Services, creates algorithms that combine a wide range of economic variables - including unemployment, personal disposable income and savings rates - with statistical trends and data from individual retailers to create a complete picture of a store’s customer base.

Haydock uses a combination of statistical methods (‘better for trends and seasonality’) and Machine Learning (ML) (‘which picks up changing patterns in buying habits’) to provide a customized range of predictions on what consumers want now and in the future. A recurrent neural network (RNN) using a Long Short-term Memory (LSTM) strategy pulls in new customer data for a particular store, category or channel (like e-commerce) and also make decisions based on past trends while ignoring ‘fluke’ events that could skew the data.

The combination of long-term statistics and pattern-spotting ML methods allows Haydock to ‘spot a turn’ in individual categories that are ‘pretty volatile from month to month’ and flag it up to retailers in advance. Whereas traditional forecasting and reporting methods concentrate on overall customer trends, this system gives retailers a granular view of what people want across each section of the store. Haydock also creates ‘specialized composites’ of different metrics - such as a ‘fear index’ that represents how concerned people are about the strength of the economy - to give retailers insight into the nuanced trends that can affect their business. By bringing in such a diverse range of factors and working on an individual category level, Haydock says that the system achieves 98% accuracy across all categories, with an extra 2% margin of error in national reports because ‘government figures are always a moving target.’

Seasonal shifts

Economic factors are a good indicator of how likely people are to spend or save their money, but with today’s connected customer base, there are far more variables to add to the mix - whether or not to buy online, if there is a cheaper option elsewhere, or what the weather will be like when you have the chance to shop. IBM’s enterprise service Metropulse combines weather data with geographical and individual store metrics to predict demand on a store-by-store basis, helping retailers with promotions, layout and when to release seasonal items. This year, for example, unseasonable snow storms in the US caught a lot of thanksgiving shoppers unawares, giving savvy retailers the opportunity to sell things like snow chains and winter coats, and put fall stock (like vests or rain jackets) on sale or in storage.

Demand is not always so easy to measure though, and e-commerce has created ‘bullet customers’ that research online, buy the product they want and then leave the store without browsing. While 90% of sales are still in-store, e-commerce is increasingly the first option for shoppers, and digital sales can provide a huge amount of insight into browsing and buying habits, although e-commerce is often left as a single sales channel in the company books. As a result, retailers are making moves to integrate the digital and physical experience, so that customers can still benefit from researching the product they want while being offered different options in-store that they may not have considered.

This ‘omnichannel approach’ can give retailers a chance to capitalize on overall trends that appear online, and also create revenue in-store using traditional methods such as changing store layout for the holidays. For example, men’s apparel (up 12.7% from last year) and consumer appliances (up 15.2%) are very buoyant categories this year, and if retailers can get customers to brave the weather and come in-store then there is far more opportunity for them to make sales over both of these categories thanks to store layout or timely promotions.

Shopping for the shoppers

In the not to distant future then, shoppers can expect a highly personalized experience based on predictions made by AI and a huge amount of data from economic, meteorological and hyper-local sources. The development of IoT applications to track footfall, create heat maps and regulate the store environment will also allow retailers to gather data on their customers in-store and online, and in turn create a more comfortable retail environment that caters precisely to customer needs and desires.

Retail forecasting, far from being a rough estimate based on what has been bought in the past, can now provide retailers with a hugely granular insight into customers’ browsing and buying intentions. With an increasingly competitive retail environment, and e-commerce providing an entirely new way for customers to shop, retailers are using data to create stores that ‘take on the personality of their customers’, using every available metric to make shopping a pleasant and profitable experience all year round.

Charles Towers-Clark is Group CEO of Pod Group, an IoT connectivity & billing software provider. His book ‘The WEIRD CEO’ covers AI & the future of work. Follow him @ctowersclark